Increasing jellyﬁsh populations: trends in Large Marine ......Anglia, Norwich NR4 7TJ, UK W. W. L....

JELLYFISH BLOOMS

Increasing jellyfish populations: trends in Large MarineEcosystems

Lucas Brotz • William W. L. Cheung •

Kristin Kleisner • Evgeny Pakhomov •

Daniel Pauly

Published online: 3 April 2012

� The Author(s) 2012. This article is published with open access at Springerlink.com

Abstract Although there are various indications and

claims that jellyfish (i.e., scyphozoans, cubozoans,

most hydrozoans, ctenophores, and salps) have been

increasing at a global scale in recent decades, a

rigorous demonstration of this has never been pre-

sented. Because this is mainly due to scarcity of

quantitative time series of jellyfish abundance from

scientific surveys, we attempt to complement such

data with non-conventional information from other

sources. This was accomplished using the analytical

framework of fuzzy logic, which allows the

combination of information with variable degrees of

cardinality, reliability, and temporal and spatial cov-

erage. Data were aggregated and analyzed at the scale

of Large Marine Ecosystem (LME). Of the 66 LMEs

defined thus far that cover the world’s coastal waters

and seas, trends of jellyfish abundance after 1950

(increasing, decreasing, or stable/variable) were iden-

tified for 45, with variable degrees of confidence. Of

those 45 LMEs, the majority (28 or 62%) showed

increasing trends. These changes are discussed in the

context of possible sources of bias and uncertainty,

along with previously proposed hypotheses to explain

increases in jellyfish.

Keywords Jellyfishes � Gelatinous zooplankton �Blooms � Pelagic cnidarians � Ctenophores �Fuzzy logic

Introduction

Jellyfish are a conspicuous, but relatively little studied

component of marine ecosystems, whose populations

fluctuate widely with ocean climate and also experi-

ence sudden outbursts known as ‘‘blooms,’’ followed

by population crashes (Purcell, 2005). There are also

recent suggestions that jellyfish may be synanthropic,

specifically, benefiting from human interactions with

the oceans, and thus may be increasing globally (Mills,

2001; Purcell et al., 2007; Pauly et al., 2009a;

Richardson et al., 2009). Previous global reviews of

Guest editors: J. E. Purcell, H. Mianzan & J. R. Frost / Jellyfish

Blooms: Interactions with Humans and Fisheries

L. Brotz (&) � K. Kleisner � D. Pauly

Sea Around Us Project, Fisheries Centre, University of

British Columbia, 2202 Main Mall, Vancouver,

BC V6T 1Z4, Canada

e-mail: [email protected]

L. Brotz � E. Pakhomov

Department of Earth and Ocean Sciences, University of

British Columbia, 6339 Stores Road, Vancouver,

BC V6T 1Z4, Canada

W. W. L. Cheung

School of Environmental Sciences, University of East

Anglia, Norwich NR4 7TJ, UK

W. W. L. Cheung

Fisheries Centre, University of British Columbia,

2202 Main Mall, Vancouver, BC V6T 1Z4, Canada

123

Hydrobiologia (2012) 690:3–20

DOI 10.1007/s10750-012-1039-7

jellyfish populations (e.g., Mills, 2001; Purcell et al.,

2007; Chudnow, 2008) show evidence of numerous

localized increases; however, for most ecosystems,

long time series of abundance measures for jellyfish

are lacking, and the perceived widespread or global

increase in jellyfish still lacks a rigorous foundation.

Establishing abundance trends for jellyfish is dif-

ficult due to a number of factors. There is a dearth of

historical information on jellyfish, because they were

usually damaged or not recorded when caught in

routine bottom trawl or zooplankton surveys (Pugh,

1989; Hay, 2006). In fact, the latter often used gear

designed to exclude jellyfish from plankton samples

(e.g., Heinle, 1965) or were based on methodologies

that explicitly recommended their removal before

analysis (e.g., Dovel, 1964). For example, a classic

manual on zooplankton sampling published by

UNESCO (1968) mentions jellyfish only once, i.e.,

‘‘Gelatinous organisms and other animals […] will

occur in the catches and these must be considered

separately from the main sample.’’

Moreover, jellyfish are difficult to sample even

when targeted (Omori & Hamner, 1982; Pierce, 2009).

As a result of their neglect in routine surveys and

marine samples, jellyfish were generally perceived as

a bothersome and unimportant component of marine

ecosystems (Pauly et al., 2009a), which then justified

their further neglect. Furthermore, despite recent

advances in research and understanding of jellyfish

ecology at local scales, such knowledge is rarely used

to evaluate possible causes or consequences of jelly-

fish blooms at larger scales, or to make predictions

(Purcell, 2009).

Their peculiar life cycles, which can result in

extremely high temporal and spatial variability in

abundance, peaking in the form of ‘‘blooms’’ (Mills,

2001; Purcell et al., 2007; Boero et al., 2008; Dawson

& Hamner, 2009; Hamner & Dawson, 2009), also

contribute to why jellyfish tend to be understudied. All

cubozoans, as well as many hydrozoans and scyph-

ozoans have a life history consisting of a sessile polyp

phase and a planktonic medusa phase. Many polyps

reproduce asexually through the process of strobila-

tion, producing multiple ephyrae which join the

zooplankton community (Arai, 1997) and rapidly grow

to become medusae (Palomares & Pauly, 2009). For

some species, the polyps may asexually bud more

polyps or form dormant cysts capable of surviving

harsh environmental conditions (Arai, 2009). These

characteristic life history traits make jellyfish suited to

highly variable environments, because they can sur-

vive when conditions are unfavorable and rapidly

reproduce when conditions are favorable (Boero et al.,

2008; Richardson et al., 2009). Siphonophores, cteno-

phores, and salps lack a polyp phase, but can also

reproduce rapidly under favorable conditions (Alldredge

& Madin, 1982; Purcell et al., 2007). Such varied

reproductive strategies make it extremely difficult to

assess jellyfish populations. Indeed, if few surveys have

been conducted to quantify medusa abundance, even less

is known about their polyps (Mills, 2001).

More attention has been paid to jellyfish in recent

years because of their interference in human enter-

prises, their ecological importance, and their benefits to

humans. Jellyfish directly interfere with many human

activities (reviewed by Purcell et al., 2007; Richardson

et al., 2009), specifically, through stings (beach

closures, tourism impacts, injuries, deaths), clogging

intakes (coastal power and desalination plants, mining

and military operations, shipping, aquaria), interfer-

ence with fishing (clogged and split nets, spoiled catch,

stung fishers, damaged gear, capsized boats), aquacul-

ture (fish deaths, pens fouled by polyps), and marine

biological surveys (interference with trawls and acous-

tic surveys). Jellyfish also have ecosystem impacts

with indirect effects on fisheries resources that are

difficult to quantify, such as their roles as predators of

zooplankton, fish eggs and ichthyoplankton, as vectors

for parasites, as food for fish, and as refugia and food

for some species of juvenile fish (interactions reviewed

by Purcell & Arai, 2001).

Some jellyfish also benefit humans (reviewed in

Purcell et al., 2007), notably as food (Hsieh et al.,

2001), and potentially for use in drugs (e.g., Sugahara

et al., 2006; Ohta et al., 2009). The discovery,

isolation, and development of a fluorescent protein

from jellyfish led to a revolution in biotechnology

(Zimmer, 2005) and a Nobel Prize (Coleman, 2010);

however, the proteins now are synthesized in the

laboratory. Unfortunately, such benefits may be out-

weighed by the direct and indirect negative impacts of

jellyfish blooms.

The lack of jellyfish population datasets covering

large temporal and spatial scales limits the scope of

inferences that can be drawn about jellyfish on a global

basis. To compensate for this, we used analytic

methods designed to allow for the inclusion of a wide

variety of information, including ‘‘anecdotal data,’’

4 Hydrobiologia (2012) 690:3–20

123

whose value is often underestimated (Pauly, 1995).

Because the majority of recently reported changes in

jellyfish populations around the globe occur in coastal

waters or semi-enclosed seas (Mills, 2001; Purcell

et al., 2007), the Large Marine Ecosystem (LME)

framework provided a suitable stratification scheme to

investigate these trends. We used the established

system of fuzzy logic to examine the evidence for

changes in jellyfish populations over recent decades.

Materials and methods

Definition of ‘‘jellyfish’’

Because the term ‘‘jellyfish’’ lacks a formal definition,

we present an operational definition used in this

analysis, which will be used to refer to both single and

multiple species. Here, the word ‘‘jellyfish’’ refers to

gelatinous zooplankton that include medusae of the

phylum Cnidaria (scyphomedusae, hydromedusae,

cubomedusae, and siphonophores) and planktonic

members of the phylum Ctenophora. We also included

the pelagic tunicates known as salps due to their

gelatinous nature, pulsed life cycles, and apparent

response to changing oceanic conditions (Loeb et al.,

1997; Atkinson et al., 2004; Lee et al., 2010).

Especially sparse time series data on pyrosomes and

doliolids prevented their inclusion in the analysis.

Other types of gelatinous zooplankton, such as

appendicularians, mollusks, and chaetognaths, were

not included in our analysis for various reasons (their

small size, life history, ecological roles, high carbon-

to-weight ratio), and the fact that they are generally not

considered jellyfish (see Mianzan & Guerrero, 2000;

Graham & Bayha, 2007; Richardson et al., 2009).

Pleustonic jellyfish, such those belonging to the genera

Physalia, Porpita, and Velella, also were excluded

because their local distribution is heavily influenced

by wind patterns (Mackie, 1974). As such, locations

reporting these species are frequently implicated in

claims of ‘‘unprecedented’’ blooms and mass beach

strandings lacking a historical context.

LME approach and the 1950 baseline

In order to examine and compare changes in jellyfish

populations, data were stratified by LME. The LME

framework defines boundaries based on ecological

criteria rather than economic or political criteria

(Sherman & Hempel, 2009). LMEs may extend from

nearshore areas, including river basins and estuaries,

out to the seaward boundaries of continental shelves or

coastal currents (Sherman & Tang, 1999). Four sets of

factors were considered when defining the physical

extent of the LME boundaries, i.e., bathymetry,

hydrography, productivity, and trophic relationships.

LMEs range from 150,000 km2 to more than 5 million

km2. To date, 66 LMEs have been described in terms

of these parameters (Sherman & Hempel, 2009), with

emphasis on fisheries (Pauly et al., 2009b, see also

www.seaaroundus.org).

In order to examine changes in jellyfish popula-

tions, a baseline must be selected. For our analysis,

changes were only considered if they occurred after

1950, notably because this was the first year for which

the Food and Agriculture Organization of the United

Nations (FAO) published its annual compendia of

global fisheries catches (which now include jellyfish),

part of an effort by the United Nations to ‘‘quantify the

world’’ (Ward et al., 2004). Also, most of the reported

changes in jellyfish populations stem from recent

decades (Mills, 2001; Purcell et al., 2007); thus, a 1950

baseline provides the contrast required for comparison

and testing of such reports. Finally, many of the

anthropogenic factors that have been suggested as

causes of recent increases in jellyfish populations have

been quantified only since the mid-twentieth century,

notably because they are derived from FAO data (e.g.,

Watson et al., 2004) and recently have been re-

expressed at the LME scale (e.g., Maranger et al.,

2008; Pauly et al., 2009b).

The jellyfish chronicles

The data used in this analysis were aggregated into

‘‘chronicles.’’ Each chronicle consists of one or

more pieces of evidence and has an associated

‘‘Abundance Trend’’ and ‘‘Confidence Index,’’ cal-

culated from scores for spatial and temporal extent,

as well as reliability. The reliability score allowed us

to consider and combine information from the

scientific, peer-reviewed literature, as well as infor-

mation gleaned from other sources (e.g., ‘‘anec-

dotes’’). These chronicles were aggregated by LME

and then analyzed using a fuzzy logic expert system

(Zadeh, 1965) to generate a ‘‘Jellyfish Index’’ for

each LME.

Hydrobiologia (2012) 690:3–20 5

123

http://www.seaaroundus.org

Multiple pieces of evidence covering similar tem-

poral and spatial scales were included as one chron-

icle, and only data that referred to changes (or lack

thereof) over several years or greater were included.

Therefore, isolated references to ‘‘lots of jellyfish’’ or

‘‘more jellyfish than last year’’ would not qualify for

inclusion due to low temporal coverage. The same

rationale was applied to populations with decreasing

or relatively stable trends, or those showing high

variability.

Increasing or decreasing trends were reported to

occur only if they were sustained. Thus, a population

of jellyfish showing a prolonged increase followed by

a similar decrease was classified as ‘‘variable.’’

Because chronicles were scored on several compo-

nents, those with no recent data (post-2000) received a

lower temporal score in order to reflect the uncertainty

of whether the identified trend has continued or not.

Data for the North Sea LME chronicles were included

here as an example. Details of all chronicles are in

Brotz (2011).

Data selection

All direct comments or measurements indicating

changes (or lack thereof) in jellyfish populations over

several years were included in the analysis; however,

indirect evidence was not included. Such indirect

evidence consists of impacts of jellyfish on human

activities such as sting events, clogging of intake pipes

for power generation, shipping, or mining operations,

as well as interference with aquaculture operations.

Although changes in the frequency of these events

may indicate changes in jellyfish populations (Purcell

et al., 2007), there can also be a consequence of

changes in sampling effort. For example, a jellyfish

bloom that interferes with an industrial operation may

actually represent a stable jellyfish population if the

industrial operation is new to the region, rather than an

actual increase in jellyfish. Therefore, isolated inter-

ference events with industrial operations were

excluded from the analysis.

Individual events related to direct interference with

fishing activities also were excluded. However, we

included information that referred to the changing

frequency of such events because we believed this to

be a strong indication of a change in jellyfish

abundance. For example, fishers in some locations

reported increasing jellyfish by-catch over years or

decades (e.g., Uye & Ueta, 2004). Because fishers

generally have a keen understanding of the marine

environment, such statements were assumed to be

reliable. In addition, for most locations with extant

fisheries, it is expected that fishers have improved their

ability to avoid catching jellyfish over time (e.g.,

Kendall, 1990; Matsushita & Honda, 2006; Nagata

et al., 2009). Thus, we believed that increases in

jellyfish by-catch observed by fishers were likely to

reflect increased jellyfish populations.

Sting data generally were not included in our

analysis, because they are problematic due to a

number of factors. First, an increase in the number

of people participating in marine activities would

increase encounter rates (Macrokanis et al., 2004). In

addition, data showing an increase in sting events

may simply be a reflection of increased reporting

(Gershwin et al., 2010). As such, an increase in sting

events may not necessarily represent an increase in the

amount of jellyfish present. Conversely, awareness

and educational campaigns, as well as the use of

jellyfish deterrents or countermeasures, can result in a

decrease in sting events without a concomitant reduc-

tion of the jellyfish population (Gershwin et al., 2010).

Therefore, sting data were excluded from the analysis,

except where they revealed temporal changes (e.g.,

increase in the stinger season) or spatial changes (e.g.,

increased distribution of jellyfish).

Abundance Trend

Each chronicle was assigned an ‘‘Abundance Trend’’

of increasing (?1), decreasing (-1), or stable/variable

(0). The trend was identified by considering changes in

integrated biomass (i.e., abundance and presence).

Therefore, increases (or decreases) in any metric

(overall biomass, frequency of occurrence, or duration

of occurrence) were considered to be indications of an

increase (or decrease). As such, more frequent blooms,

larger blooms, longer-lasting blooms, and range

expansions (and their converses) all were included.

When knowledge was available on multiple species

over similar scales, the overall biomass of jellyfish

within the ecosystem was considered. In addition,

small, non-abundant hydromedusae received lower

scores due to the fact that they are less likely to affect

the overall biomass of jellyfish in the ecosystem.

Supporting evidence for each chronicle consisted

of either qualitative or quantitative information.


123

Chronicles with qualitative data as their primary

source were classified based on the description of the

jellyfish population in question (Table 1). For chron-

icles with quantitative records, such as multi-year

datasets with values for relative abundance or bio-

mass, a general linear regression analysis was per-

formed. If the slope of the linear regression

(abundance against time) was positive and signifi-

cantly different from zero (P \ 0.05), the dataset was

considered to represent an increase. Conversely, a

significant negative slope constituted a decrease. If the

slope of the linear regression was not statistically

significant, the dataset was classified as stable/

variable.

Scoring the chronicles

Each chronicle was scored according to a set of

rules (Table 1) based on temporal coverage (‘‘Time

score’’), spatial coverage (‘‘Space score’’), and

reliability (‘‘Reliability score’’) with the reliability

for invasive species scored differently. These scores

were used to calculate the overall ‘‘Confidence

Index,’’ a measure of the level of certainty for each

chronicle.

Invasive species

Here, we consider invasive species to represent those

that have been declared as non-indigenous by experts.

The presence of invasive species of jellyfish was

assumed to represent an increase in jellyfish biomass

(Abundance Trend = 1). With this assumption, it is

clearly important to understand if an invasive species

is truly established because some invaders can appear

briefly in a particular area, but not be detected

thereafter. Knowledge of such events was assumed

to represent no change in a jellyfish population

(Abundance Trend = 0), rather than an increase, as

the excess biomass due to the invader presumably

vanishes if the species is no longer detected. However,

the possibility of repeated detection persists due to

potential establishment by cryptic polyps or succes-

sive invasions, as is likely with Phyllorhiza sp. in the

South Brazil Shelf LME (Haddad & Nogueira, 2006).

The possibility also exists that invasive species of

jellyfish could cause a reduction in native jellyfish

biomass. However, no evidence of such an event was

found (but see Brotz, 2011 for discussion).

Chronicles that pertained to invasive species were

scored similarly to other chronicles on the basis of

time and space, but differently for reliability. The

contribution to an increase in jellyfish biomass due to

an invader was weighted by the ‘‘Invasive reliability

score’’ (Table 1) to provide a more accurate estimate

of the total change in jellyfish biomass. The assump-

tions and weighting factors were designed to avoid an

overemphasis on invasive species. However, the

invasive jellyfish accounted for in this analysis

represent a conservative estimate, because it is likely

that far more invasions have occurred than have been

documented due to incomplete treatment, unusual life

histories, and species crypsis (Holland et al., 2004;

Dawson et al., 2005; Graham & Bayha, 2007).

Invasive species were treated separately during anal-

yses, allowing assessment of their contribution to the

results. Consistent with the baseline selected for the

analysis, species that invaded regions prior to 1950

were excluded.

Fuzzy logic expert system

Scores and chronicles were combined using a series of

rule sets and fuzzy logic. The steps are outlined below,

and the methodology diagramed using the North Sea

LME as an example (Fig. 1). Fuzzy set theory,

originally developed by Zadeh (1965), is now firmly

established in engineering and science (e.g., Lee,

1990; van der Werf & Zimmer, 1998; Cheung et al.,

2007), and fuzzy models are increasingly being used

for ecological applications (Jørgensen, 2008). Fuzzy

set theory allows the representation of variables

according to a gradation or degree of membership,

rather than the classic ‘‘true’’ or ‘‘false’’ membership

of conventional Boolean sets. In addition, fuzzy logic

allows a conclusion to be reached with an associated

gradation or degree of belief. As such, fuzzy set theory

and logic provide an ideal system for combining

information of variable cardinality and confidence.

Adriaenssens et al. (2004) reviewed fuzzy set theory

used in ecosystem studies and Brotz (2011) details the

specific methodology used in our study.

Variables with differing degrees of confidence were

combined using the ‘‘MYCIN’’ method, an asymptotic

accumulation of the degree of belief, after Buchanan

& Shortliffe (1984). This knowledge accumulation

method is not affected by the order in which evidence

is combined, and can be defined as:


123

Degree of beliefnþ1 ¼ Evidencen

þ 1� Evidencenð Þ � Evidencenþ1½ �

where Degree of beliefn?1 is the membership in the

conclusion after combining the membership from

Evidencen and Evidencen?1. The membership for any

number of pieces of evidence can thus be combined to

yield a final membership (i.e., degree of belief) in the

conclusion.

The three scores for each jellyfish chronicle (time,

space, and reliability) were combined using a fuzzy

rule set, or combination matrix, to yield a ‘‘Confidence

Index’’ (Table 2). The combination matrix used treats

all three scores equally, and therefore represents all

Table 1 Rule sets defined for analysis of jellyfish population trends in LME

Definition

Abundance Trend rule set

Abundance Trend

-1 (decrease) Decrease in overall biomass, relative abundance, frequency of occurrence or duration of occurrence

0 (stable/

variable)

Stable or no obvious trend

?1 (increase) Increase in overall biomass, relative abundance, frequency of occurrence or duration of occurrence

Time score rule set

Time score

Low Multiyear trend \ 5 years; recent and unrepeated bloom that has not occurred previously; unclear timeframe; no

recent data (post-2000)

Medium Short term (5–9 years)

High Medium term (10–14 years)

Very high Long term (C15 years)

Space score rule set

Space score

Low Singular location or small region within LME (\200 km wide)

Medium Large region or two disparate locations within LME ([200 km apart)

High Three or more disparate locations within LME; wide-scale sampling in at least half of LME

Very high Wide-scale sampling of LME

Reliability score rule set

Reliability score

Low Lifeguard or NGO commentary; species unlikely to contribute significantly to biomass; high uncertainty;

documented anthropogenic polyp habitat

Medium Marine professional commentary (e.g., fisher)

High Marine scientist commentary; synthesized knowledge; ‘‘bookend’’ (i.e., non-continuous) scientific data

Very high Scientific data of numerous or dominant species; well-documented frequency of blooms

Invasive reliability score rule set

Reliability score

Low Uncertainty of invasiveness or species is unlikely to contribute significantly to biomass (e.g., small

hydromedusae)

Medium Documented invasive species or newly-blooming species (without knowledge of other species in ecosystem) or

unsuccessful establishmenta

High Thriving invasive species

Very high Known dominant species

Rule sets here include: Abundance Trend, Time score, Space score, and Reliability scores for native and invasive species. Additional

parameters are in Tables 2 and 3a Abundance Trend = 1 in all invasive cases except for unsuccessful establishment (where Abundance Trend = 0 and Invasive

reliability score = medium)


123

possible combinations of scores. Thus, each chronicle

has an associated Abundance Trend representing the

direction of change for the jellyfish population in

question and a Confidence Index representing the

Degree of Belief. Chronicles included in the North Sea

LME (Table 3) are depicted in the fuzzy expert system

diagram (Fig. 1). Details of all chronicles used in this

analysis are in Brotz (2011).

Within each LME, chronicles that had the same

Abundance Trend were combined to yield a Belief

Index, which was derived by converting the Confi-

dence Index value for each chronicle into a member-

ship (Degree of Belief; Table 4) and subsequently

combining these memberships using MYCIN. The

resulting Belief Indexes for each Abundance Trend

were used to select an appropriate Belief Profile

(Table 4). The Belief Profiles used in the fuzzy expert

system were membership functions designed to rep-

resent the Degree of Belief over a continuous scale of

-100 to ?100, with negative scores representing

declining jellyfish populations and positive scores

representing increasing populations. These asymmet-

rical Belief Profiles therefore provide a representation

of the accumulated evidence for each particular trend,

including both the quantity and the relative certainty

of the evidence. Within each LME, one profile was

selected for each Abundance Trend, as long as there

was supporting evidence (i.e., Belief Index[0). Thus,

an LME could have 1, 2, or 3 profiles as inputs for the

fuzzy expert system, depending on whether or not

there were chronicles supporting each Abundance

Trend. The Belief Profiles were combined using the

MYCIN method to yield a final Degree of Belief

profile for each LME. This profile contained informa-

tion about the evidence within each LME over all

Abundance Trends. To calculate a final Jellyfish

Index, the centroid-weighted method (Cox, 1999)

was used to ‘‘defuzzify’’ the final profile.

Uncertainty

The confidence in the Jellyfish Index was quantified by

the Degree of Belief at the centroid value (the

‘‘Confidence Factor’’) and the associated values at

Degree of Belief = 0.25 (the confidence limits). The

difference between the confidence limits is defined as

the ‘‘Confidence Interval.’’ If a particular profile did

not reach a Degree of Belief = 0.25 due to lack of

Fig. 1 Schematic diagram of the fuzzy expert system used in the analysis of jellyfish population trends by LME, with the North Sea

LME represented as an example


123

evidence (e.g., Gulf of California LME), the upper and

lower confidence limits were selected where the

Degree of Belief falls to zero. Use of these two

measures of uncertainty (the Confidence Factor and

the Confidence Interval) provided information about

both the strength of the data within an LME and how

consistent was the observed trend (if any). These

would be similar to measures of ‘‘accuracy’’ and

‘‘precision,’’ i.e., a high Confidence Factor represented

a robust conclusion and could be interpreted as

accurate. Similarly, a small Confidence Interval would

indicate that the chronicles included in a particular

LME have comparable trends and were therefore

precise. The combination of these two measures

ultimately defined the overall confidence in the

Jellyfish Index for each LME; thus, we defined a

‘‘Confidence Quotient’’ as equal to the Confidence

Factor divided by the Confidence Interval. Conclu-

sions with a Confidence Quotient[1 were classified as

‘‘high certainty,’’ while those with a Confidence

Quotient\1 were classified as ‘‘low certainty.’’

Based on the Belief Profiles used in the analysis,

Jellyfish Indexes could range from a minimum of -70

to a maximum of ?70. LMEs with a Jellyfish Index of

greater than ?10 were classified as increases, while

those with a Jellyfish Index less than -10 were

classified as decreases. LMEs with a Jellyfish Index

between -10 and ?10 were classified as stable/

variable, indicating they did not show an increasing or

decreasing trend. These thresholds were chosen in

order to ensure there was sufficient evidence to

suggest a trend.

Results

A total of 138 jellyfish chronicles were included in the

analysis, distributed unevenly over 45 LMEs. Results

including both native and invasive species are pre-

sented in Table 5. Of the 45 LMEs, 28 (62%) showed

increasing trends, while only 3 (7%) showed decreas-

ing trends. The remaining 14 LMEs (31%) were

classified as stable/variable, showing neither increas-

ing nor decreasing trends (Fig. 2).

Out of the 28 LMEs exhibiting increases, 10 were

classified as high certainty (Confidence Quotient [ 1)

and 18 as low certainty. Of the 14 LMEs with stable/

variable trends, 4 were of high certainty and 10 were of

low certainty. The Humboldt Current LME was the

only system to exhibit a decrease associated with a

high certainty.

The results are similar when normalized by area of

the LMEs; 21% of the total area included represented

regions with increases of high certainty, while

increases of low certainty represented 45%. Stable/

variable regions represented 28% of the total area

included, while the remaining 6% was associated with

decreases.

Effects of invasive species

Invasive species were separated from the analysis to

examine their effects on the results (Tables 5, 6).

Invasive species of jellyfish were reported in 21

LMEs. In eight of those, the inclusion of invasive

species had a negligible contribution to the results and

did not affect the Jellyfish Index. By contrast, the

inclusion of invasive species was responsible for the

conclusion of low certainty increases in four LMEs

(Gulf of Mexico, Southeast U.S. Continental Shelf,

Table 2 Combination matrix used to combine scores, yielding

a single Confidence Index

Score A Score B Score C Confidence Index

Low Low Low Low

Low Low Medium Low

Low Low High Medium-low

Low Low Very high Medium-low

Low Medium Medium Medium-low

Low Medium High Medium

Low Medium Very high Medium

Low High High Medium

Low High Very high Medium-high

Low Very high Very high Medium-high

Medium Medium Medium Medium

Medium Medium High Medium-high

Medium Medium Very high Medium-high

Medium High High Medium-high

Medium High Very high High

Medium Very high Very high High

High High High High

High High Very high High

High Very high Very high Very high

Very high Very high Very high Very high

Scores for time, space, and reliability were treated equally, and

therefore the matrix represents all possible combinations of

scores


123

Ta

ble

3E

xam

ple

of

info

rmat

ion

con

stit

uti

ng

chro

nic

les

use

din

the

anal

ysi

s

Co

nfi

den

ce

Ind

ex

Ab

un

dan

ce

Tre

nd

Tim

e

sco

re

Sp

ace

sco

re

Rel

iab

ilit

y

sco

re

Co

un

try

Lo

cati

on

Dat

esS

pec

ies

Mai

nso

urc

eA

dd

itio

nal

sou

rces

Med

ium

-

low

-1

Lo

wL

ow

Hig

hU

nit

ed

Kin

gd

om

Th

ames

Est

uar

y

Sin

ce1

98

5

(dat

a

19

77

–1

99

2)

Au

reli

asp

.

(Ple

uro

bra

chia

pil

eus

is

var

iab

le)

Att

rill

&T

ho

mas

(19

96

)

Med

ium

-

hig

h

0L

ow

Hig

hV

ery

hig

hN

um

ero

us

Wid

esca

le

sam

pli

ng

in

hal

fo

fL

ME

19

71

–1

98

6N

um

ero

us

Hay

etal

.(1

99

0)

Ly

nam

etal

.(2

00

4,

20

05

)

Med

ium

-

low

0V

ery

hig

h

Lo

wL

ow

aG

erm

any

Hel

go

lan

d,

Ger

man

Big

ht

19

75

–1

99

3;

19

75

–2

00

2

Nu

mer

ou

sG

rev

eet

al.

(19

96

,

20

04

)

Gre

ve

(19

94

)an

d

Sch

lute

ret

al.

(20

10

)

Ver

yh

igh

?1

Ver

y

hig

h

Ver

y

hig

h

Hig

hN

um

ero

us

En

tire

LM

ES

ince

19

80

sL

ikel

yA

gla

nth

ad

igit

ale

Att

rill

etal

.(2

00

7)

and

Lic

and

ro

etal

.(2

01

0)

Att

rill

&E

dw

ard

s

(20

08

)an

dH

add

ock

(20

08

)

Med

ium

-

hig

h

?1

Ver

y

hig

h

Lo

wV

ery

hig

hN

eth

erla

nd

sT

exel

Isla

nd

Rec

ent

dec

ades

Nu

mer

ou

sv

anW

alra

ven

etal

.

(20

10

)

Med

ium

-

hig

h

?1

Ver

y

hig

h

Lo

wH

igh

No

rway

Lu

refj

ord

enS

ince

19

70

sP

erip

hyl

lap

erip

hyl

laF

oss

a(1

99

2)

Yo

un

gb

luth

&B

amst

edt

(20

01

)an

dS

ørn

es

etal

.(2

00

7)

Med

ium

-

hig

h

?1

Ver

y

hig

h

Lo

wH

igh

Den

mar

kL

imfj

ord

enS

ince

19

80

sA

ure

lia

spp

.(&

oth

ers)

Rii

sgar

det

al.

(20

12

)

Ho

ffm

ann

(20

05

)an

d

Mø

ller

&R

iisg

ard

(20

07

a,b

)

Med

ium

-

hig

h

?1

Med

ium

Hig

hM

ediu

mN

um

ero

us

Nu

mer

ou

sS

ince

atle

ast

20

05

Mn

emio

psi

sle

idyi

(in

vas

ive)

Oli

vei

ra(2

00

7)

and

Ten

dal

etal

.

(20

07

)

Faa

sse

&B

ayh

a(2

00

6),

Han

sso

n(2

00

6),

Bo

ersm

aet

al.

(20

07

),

Ho

sia

(20

07

),an

d

Rii

sgar

det

al.

(20

07

)

Det

ails

are

pre

sen

ted

her

efo

rch

ron

icle

su

sed

for

the

No

rth

Sea

LM

Eo

nly

aS

ee(B

rotz

,2

01

1)

for

dis

cuss

ion


123

Caribbean Sea, and Baltic Sea), because the exclusion

of invaders changed the classification of these LMEs

from increasing to stable/variable. Similarly, invaders

were responsible for the low certainty increase in the

East Brazil Shelf LME because there were no data for

native species. The Insular Pacific-Hawaiian LME

exhibited an increase due to native species; however,

the inclusion of invasive species increased the cer-

tainty of the conclusion to high. In the remaining

LMEs, the inclusion of invasive species increased the

Jellyfish Index by variable amounts, but did not alter

the conclusions.

Considering the effects of jellyfish

overexploitation

Interestingly, several of the chronicles that were clas-

sified as decreases in the analysis (Abundance

Trend = -1) concerned jellyfish species that have been

harvested for food, science, or unique proteins, and have

subsequently declined, possibly as a result of overfish-

ing. Only four chronicles had a primary source of

evidence that directly attributed a decrease to overex-

ploitation; therefore, these chronicles were treated

separately in the analysis. In the Arabian Sea LME,

the inclusion of overfishing of jellyfish reduced the

Jellyfish Index sufficiently to alter the trend conclusion

from increasing to stable/variable (both conclusions of

low certainty). Inclusion of overfishing of jellyfish for

the Bay of Bengal LME resulted in no change to the

Jellyfish Index. The South China Sea and East Central

Australian Shelf LMEs showed a reduced Jellyfish

Index when overfishing of jellyfish was included;

however, this reduction was not sufficient to classify

these LMEs as decreases and they remained classified as

stable/variable. Thus, in the majority of locations where

overfishing of jellyfish could be identified, it did not alter

the conclusions of the analysis.

Discussion

This study represents the first rigorous demonstration

that jellyfish populations appear to be increasing in

coastal ecosystems worldwide, as previously sug-

gested (Mills, 2001; Purcell et al., 2007; Pauly et al.,

2009a; Richardson et al., 2009). Of the 45 LMEs

included in our analysis, 28 (62%) showed increasing

trends, while only 3 (7%) showed decreasing trends.

The remaining 14 LMEs (31%) were classified as

stable/variable, with no obvious trend. These results

suggest that while increases of jellyfish populations

are not universal, they are both numerous and

widespread. Of the 21 LMEs that were not included

in our analysis, most were from the Arctic (11),

Australia (4), and the South Pacific (3). Therefore, our

results represent extensive spatial coverage of the

world’s coastal ecosystems. While only 33% of the

conclusions are of high certainty, the majority of those

(10 of 15) were in LMEs that showed increasing

trends. In addition to demonstrating that jellyfish

populations have increased in numerous ecosystems

around the world, our analysis also underscored the

fact that information on jellyfish abundance is poor

over much of the globe. Thus, we must strive to learn

more about these important creatures, especially given

the fact that they seem to be one of the few groups of

organisms that may benefit from the continued

anthropogenic impacts on the world’s biosphere.

Defining an ‘‘increase’’

Information used in the analysis was weighted by time,

space, and reliability to reflect the relative contribution

Table 4 Rule sets used in the fuzzification process of the

fuzzy expert system

Confidence Index Degree of Belief

(per chronicle)

Low 0.0156

Medium-low 0.0313

Medium 0.0625

Medium-high 0.125

High 0.25

Very high 0.5

Belief Index Belief Profile

0 None

0.01–0.09 Low

0.1–0.19 Medium-low

0.2–0.34 Medium

0.35–0.49 Medium-high

0.50–0.59 High

0.60–1 Very high

Rule sets here include the Degree of Belief membership

according to the Confidence Index for each chronicle and the

Belief Profile selection according to the Belief Index. See text

and Fig. 1 for additional information


123

Table 5 Results of analysis of jellyfish population trends by LME including both native and invasive species

LME

ID

LME name Trend

conclusion

Conclusion

certainty

Jellyfish

Index

Confidence

Quotient

Confidence

Factor

Lower

limit

Upper

limit

Interval

1 East Bering Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50

2 Gulf of Alaska Stable/

variable

Low 7.06 0.80 0.58 -35.00 37.24 72.24

3 California Current Increase Low 25.55 0.63 0.73 -31.25 85.00 116.25

4 Gulf of California Increase Low 35.87 0.13 0.13 0.00 100.00 100.00

5 Gulf of Mexico Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25

6 Southeast US

Continental Shelf

Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25

7 Northeast US

Continental Shelf

Increase High 52.52 1.58 0.83 43.75 96.25 52.50

8 Scotian Shelf Stable/

variable

High 0.00 1.07 0.67 -31.25 31.25 62.50

9 Newfoundland-

Labrador Shelf

Stable/

variable

High 0.00 1.54 0.83 -27.00 27.00 54.00

10 Insular Pacific-

Hawaiian

Increase High 54.84 1.13 0.67 25.63 85.00 59.37

11 Pacific Central-

American Coastal

Increase Low 41.74 0.77 0.30 12.50 51.25 38.75

12 Caribbean Sea Increase Low 13.60 0.81 0.31 3.00 41.26 38.26

13 Humboldt Current Decrease High -42.80 1.26 0.71 -91.00 -34.50 56.50

14 Patagonian Shelf Increase Low 47.90 0.87 0.50 17.50 75.00 57.50

15 South Brazil Shelf Stable/

variable

Low 7.06 0.80 0.58 -35.00 37.24 72.24

16 East Brazil Shelf Increase Low 35.87 0.13 0.13 0.00 100.00 100.00

18 West Greenland

Shelf

Decrease Low -35.87 0.13 0.13 -100.00 0.00 100.00

21 Norwegian Sea Increase Low 41.74 0.70 0.27 12.50 51.25 38.75

22 North Sea Increase Low 35.89 0.22 0.30 -40.67 96.25 136.92

23 Baltic Sea Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25

24 Celtic-Biscay Shelf Increase Low 36.94 0.44 0.56 -37.50 91.00 128.50

25 Iberian Coastal Stable/

variable

Low 7.06 0.80 0.58 -35.00 37.24 72.24

26 Mediterranean Sea Increase Low 43.95 0.22 0.30 -37.50 96.25 133.75

28 Guinea Current Increase Low 35.87 0.13 0.13 0.00 100.00 100.00

29 Benguela Current Increase High 54.84 1.15 0.67 26.63 85.00 58.37

30 Agulhas Current Stable/

variable

Low 0.00 0.71 0.50 -35.00 35.00 70.00

31 Somali Coastal

Current

Stable/

variable

Low 0.00 0.44 0.33 -37.50 37.50 75.00

32 Arabian Sea Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25

34 Bay of Bengal Increase Low 14.57 0.52 0.58 -37.24 75.00 112.24

35 Gulf of Thailand Increase Low 35.87 0.13 0.13 0.00 100.00 100.00

36 South China Sea Stable/

variable

Low 8.86 0.56 0.44 -37.50 40.67 78.17

40 Northeast Australian

Shelf

Increase Low 35.87 0.13 0.13 0.00 100.00 100.00


123

to a change in jellyfish populations within each LME.

As a consequence of the methods used and the

inclusion of anecdotal data, the results reflect the

degree of belief that any particular jellyfish population

has changed or not, rather than the magnitude of those

changes. Therefore, observations of ‘‘more’’ jellyfish

Fig. 2 Map of population trends of native and invasive species of

jellyfish by LME. Red increase (high certainty), orange increase

(low certainty), green stable/variable, blue decrease, grey no data.

Circles represent discrete chronicles with relative sizes reflecting

the Confidence Index. Circle locations are approximate, as some

were shifted to avoid overlap; the circle for the Antarctic LME

summarizes circumpolar observations

Table 5 continued

LME

ID

LME name Trend

conclusion

Conclusion

certainty

Jellyfish

Index

Confidence

Quotient

Confidence

Factor

Lower

limit

Upper

limit

Interval

41 East Central

Australian Shelf

Stable/

variable

Low 0.00 0.71 0.50 -35.00 35.00 70.00

42 Southeast Australian

Shelf

Stable/

variable

Low 8.86 0.56 0.44 -37.50 40.67 78.17

47 East China Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50

48 Yellow Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50

49 Kuroshio Current Increase High 35.34 1.13 0.67 25.63 85.00 59.37

50 Sea of Japan Increase High 61.84 1.47 0.83 34.50 91.00 56.50

51 Oyashio Current Decrease Low -14.13 0.75 0.65 -51.25 35.00 86.25

52 Sea of Okhotsk Stable/

variable

High 6.25 1.55 0.86 -27.00 28.56 55.56

53 West Bering Sea Stable/

variable

Low -7.49 0.40 0.50 -75.00 51.25 125.25

60 Faroe Plateau Stable/

variable

High 0.00 1.54 0.83 -27.00 27.00 54.00

61 Antarctic Increase High 61.84 1.47 0.83 34.50 91.00 56.50

62 Black Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50

63 Hudson Bay Stable/

variable

Low 0.00 0.44 0.33 -37.50 37.50 75.00


123

may not necessarily mean there were really ‘‘more

jellyfish’’ if the observations were not normalized by

effort. Nonetheless, we expected that these factors

were correlated, as changes of larger magnitude were

assumed to be more noticeable and thus have more

supporting evidence. Only after accepting this

assumption should this analysis be considered to

reflect real ‘‘increases’’ and ‘‘decreases.’’

Jellyfish populations are extremely variable on both

temporal and spatial scales, due to their peculiar

ecology. Thus, even LMEs showing pronounced

increases in jellyfish populations with ‘‘high cer-

tainty’’ may also experience dramatic declines over

short timescales. For example, the trend in the East

Bering Sea LME was classified as an increase based on

a regression analysis, but jellyfish in the Bering Sea

declined dramatically after 2000 (Brodeur et al.,

2008). Despite this decline, jellyfish abundance in

this LME appears sustained above the levels observed

in the 1980s and the increase remains significant.

Other long-term studies show similar variability, such

as the 37-year dataset from Peru (Quinones et al.,

2010). Jellyfish populations in that system appear

tightly correlated with El Nino events, but the data

exhibited a decline (see Brotz, 2011). Even the well-

documented increase in blooms of the giant jellyfish

Table 6 Results of analysis of jellyfish population trends by LME including native species only (effects of invasive species

excluded; only those LMEs that had invasive species are shown)

LME

ID

LME name Trend

conclusion

Conclusion

certainty

Jellyfish

Index

Confidence

Quotient

Confidence

Factor

Lower

limit

Upper

limit

Interval

3 California Current Increase Low 19.82 0.73 0.78 -31.25 75.00 106.25

5 Gulf of Mexico Stable/

variable

Low 7.06 0.80 0.58 -35.00 37.24 72.24

6 Southeast US

Continental Shelf

Stable/

variable

Low 7.06 0.80 0.58 -35.00 37.24 72.24

7 Northeast US

Continental Shelf

Increase High 52.52 1.58 0.83 43.75 96.25 52.50

10 Insular Pacific-

Hawaiian

Increase Low 47.90 0.87 0.50 17.50 75.00 57.50

11 Pacific Central-

American Coastal

Increase Low 35.87 0.09 0.09 0.00 100.00 100.00

12 Caribbean Sea Stable/

variable

Low 0.00 0.17 0.17 -50.00 50.00 100.00

13 Humboldt Current Decrease High -61.84 1.47 0.83 -91.00 -34.50 56.50

14 Patagonian Shelf Increase Low 47.90 0.87 0.50 17.50 75.00 57.50

15 South Brazil Shelf Stable/

variable

Low 0.00 0.71 0.50 -35.00 35.00 70.00

16 East Brazil Shelf No data

21 Norwegian Sea Increase Low 41.74 0.70 0.27 12.50 51.25 38.75

22 North Sea Increase Low 35.89 0.22 0.30 -40.67 96.25 136.92

23 Baltic Sea Stable/

variable

Low 0.00 0.71 0.50 -35.00 35.00 70.00

25 Iberian Coastal Stable/

variable

Low 0.00 0.71 0.50 -35.00 35.00 70.00

26 Mediterranean Sea Increase Low 31.02 0.54 0.66 -37.50 85.00 122.50

42 Southeast Australian

Shelf

Stable/

variable

Low 8.86 0.56 0.44 -37.50 40.67 78.17

47 East China Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50

48 Yellow Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50

49 Kuroshio Current Increase High 35.34 1.13 0.67 25.63 85.00 59.37

62 Black Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50


123

(Nemopilema nomurai) in East Asia has not been

persistent, because blooms have not occurred every

year (Uye et al., 2010). Clearly then, increases or

decreases may actually represent a trend during only

part of a cycle, and may reverse over longer

timeframes (Purcell, 2012).

With such high population variability, poor sam-

pling frequency in either the past or present could

dramatically affect the detection of true trends. To

account for these concerns, attempts were made to

ensure chronicles used in the analysis were of sufficient

duration and up to date wherever possible. Therefore,

chronicles covering longer timescales and those with

up-to-date information had more influence on the

results. Nonetheless, few datasets of jellyfish abun-

dance span multiple decades; therefore, our results

represent only a rough estimate of true jellyfish

population dynamics. Moreover, the possibility of a

reporting bias, whereby newsworthy blooms or

increases of jellyfish were reported, but absences and

stable or declining populations were not, could tend to

overestimate increases. However, the methods used in

our analysis were designed to minimize this effect. For

instance, episodic blooms were not included unless a

temporal component of at least several years was

identified. In addition, as mentioned above, these

temporal components were scored based on whether

they represent recent trends and are of significant

duration. Interference events with human activities,

which are typically newsworthy, also were not

included unless the information was in a clear histor-

ical context. Finally, much of the anecdotal informa-

tion used in the analysis was gleaned from targeted

interviews (e.g., Uye & Ueta, 2004; Nagata et al., 2009;

Pramod, 2010). Because numerous responses in those

interviews indicated stable populations, they were

assumed to represent a relatively unbiased source of

information where scientific data were lacking.

The fact that jellyfish are typically part of the

zooplankton makes them vulnerable to changes in

oceanic current patterns. The presence or the absence

of a bloom may simply be due to relocation; thus, an

increase observed in one location may be concomitant

with a decrease in another. If an increase is observed

but a decrease is not, one may come to a false

conclusion that jellyfish have increased. Whenever

there was evidence of such an explanation, the

chronicle was not included. An example is a recent

quote from of a fisher in Florida who said he was

seeing more sea nettles (Chrysaora sp.) now than in

the preceding decades. However, this could be due to

the relocation of the population normally observed

elsewhere in the Gulf of Mexico (Spinner, 2010). Even

without knowledge of such events, the analysis was

not overly sensitive to that pitfall, because only multi-

year data from the same location were used. As

chronicles were either up-to-date or scored with low

reliability, increases due to spatial redistributions

would have to be sustained. In addition, chronicles

based on information over short time periods or from

single locations were also scored lower, thereby

minimizing the effect on the results.

Possible causes of increasing jellyfish populations

Jellyfish have bloomed for hundreds of millions of years

(Hagadorn et al., 2002; Young & Hagadorn, 2010) and

are a natural presence in healthy ecosystems. Many

jellyfish populations are known to fluctuate with oceanic

climate (reviews in Purcell, 2005, 2012). There are also

suggestions that jellyfish may benefit from anthropo-

genic pressures on the marine environment (Mills, 1995;

2001; Purcell et al., 2007; Pauly et al., 2009a; Richard-

son et al., 2009, Purcell, 2012). Suggested causes

include eutrophication, overfishing, global warming,

habitat modification, aquaculture, salinity changes,

ocean acidification, and of course, translocation.

Invasive species of jellyfish were reported in 21 of

45 LMEs in this analysis (47% of the systems

included). For the most part, invasive species were

not responsible for the observed increases reflected in

the results; however, the widespread detections dem-

onstrate that jellyfish are truly global invaders of

significant concern. Thriving populations of invasive

jellyfish in systems like the Mediterranean and Black

Seas should serve as warnings for other ecosystems

around the globe, and it is likely that far more invasions

have occurred than are reported (Holland et al., 2004;

Dawson et al., 2005; Graham & Bayha, 2007).

There is clearly no single cause of increasing

jellyfish blooms. For example, populations of Aurelia

sp. appear tightly correlated with aquaculture opera-

tions in Tapong Bay, Taiwan (Lo et al., 2008), whereas

recent increased blooms of Aurelia sp. in Tokyo Bay,

Japan are more likely due to the effects of eutrophi-

cation (Nomura & Ishimaru, 1998; Ishii et al., 2008).

In addition, possible causes may work in concert,

synergistically creating conditions that benefit


123

jellyfish (Purcell et al., 2007; Richardson et al., 2009;

Pauly et al., 2009a; Purcell, 2012). Limited knowledge

of jellyfish ecology, especially of benthic or sessile

stages, inhibits our ability to draw conclusions

regarding possible anthropogenic causes of jellyfish

blooms. Nonetheless, the results of this analysis

present a unique opportunity to examine commonal-

ities using the LME framework. An important task will

be to investigate possible linkages between anthropo-

genic stresses and increasing jellyfish populations as

identified in this study.

Taxonomic concerns

The term ‘‘jellyfish,’’ according the definition used

here, refers to specimens from several phyla (Cnidaria,

Ctenophora, and Chordata). Such organisms are

obviously extremely distant phylogenetic relatives;

therefore, grouping them under the umbrella term

‘‘jellyfish’’ is problematic. First, the use of such a term

ignores taxonomy. The changes evident in the results

of this analysis should not only be viewed in their

entirety but also in the contexts of ecology and

evolution. Without proper taxonomic resolution, a

deeper and more meaningful understanding of the

mechanisms and consequences involved may be

unattainable (Haddock, 2004). Second, using a broad

category also runs the risk of inferring attributes of a

larger group of organisms based only on a handful of

species. Such ‘‘errors of commission’’ (Dawson, 2010)

could preclude robust conclusions if they are not made

in the light of evolution. Generalizations concerning

such a broad group of organisms will certainly have

exceptions (Bayha & Dawson, 2010), and we must be

careful not to ignore these differences by focusing

only on commonalities.

Despite these concerns, there is also value in

generalized results. Notwithstanding their phyloge-

netic diversity, jellyfish share many similarities. If the

increasing trends identified in this analysis are indeed

caused by anthropogenic factors, raising awareness of

the issues and developing a deeper understanding of

the mechanisms involved should be priorities.

Conclusions

Jellyfish populations appear to be increasing in the

majority of the world’s coastal ecosystems and seas.

While these increases are conspicuous in several

locations, even basic knowledge of jellyfish popula-

tions in most regions is poor. Many of the observed

increases appear linked to human activities, but the

mechanisms involved remain poorly understood.

Because jellyfish populations can have important

impacts on human activities and marine ecosystems,

it is of paramount importance that we rapidly increase

our understanding of these creatures.

Acknowledgments We thank the numerous scientists and

marine professionals around the globe who kindly donated their

knowledge and opinions of jellyfish populations. We also thank

two anonymous reviewers and the guest editors of this volume

whose comments improved the manuscript. This is a

contribution from the Sea Around Us Project, a collaboration

between the University of British Columbia’s Fisheries Centre

and the Pew Environment Group.

Open Access This article is distributed under the terms of the

Creative Commons Attribution License which permits any use,

distribution, and reproduction in any medium, provided the

original author(s) and the source are credited.

References

Adriaenssens, V., B. D. Baets, P. L. M. Goethals & N. D. Pauw,

2004. Fuzzy rule-based models for decision support in

ecosystem management. Science of the Total Environment

319: 1–12.

Alldredge, A. L. & L. P. Madin, 1982. Pelagic tunicates -

unique herbivores in the marine plankton. BioScience 32:

655–663.

Arai, M. N., 1997. A Functional Biology of Scyphozoa. Chap-

man and Hall, London: 300 pp.

Arai, M. N., 2009. The potential importance of podocysts to the

formation of scyphozoan blooms: a review. Hydrobiologia

616: 241–246.

Atkinson, A., V. Siegel, E. Pakhomov & P. Rothery, 2004.

Long-term decline in krill stock and increase in salps

within the Southern Ocean. Nature 432: 100–103.

Attrill, M. J. & M. Edwards, 2008. Reply to Haddock, S. H. D.

Reconsidering evidence for potential climate-related

increases in jellyfish. Limnology and Oceanography 53:

2763–2766.

Attrill, M. J. & R. M. Thomas, 1996. Long-term distribution

patterns of mobile estuarine invertebrates (Ctenophora,

Cnidaria, Crustacea: Decapoda) in relation to hydrological

parameters. Marine Ecology Progress Series 143: 25–36.

Attrill, M. J., J. Wright & M. Edwards, 2007. Climate-related

increases in jellyfish frequency suggest a more gelatinous

future for the North Sea. Limnology and Oceanography 52:

480–485.

Bayha, K. M. & M. D. Dawson, 2010. New family of allomorphic

jellyfishes, Drymonematidae, emphasizes evolution in the


123

functional morphology and trophic ecology of gelatinous

zooplankton. Biological Bulletin 219: 249–267.

Boero, F., J. Bouillon, C. Gravili, M. P. Miglietta, T. Parsons &

S. Piraino, 2008. Gelatinous plankton: irregularities rule

the world (sometimes). Marine Ecology Progress Series

356: 299–310.

Boersma, M., A. M. Malzahn, W. Greve & J. Javidpour, 2007.

The first occurrence of the ctenophore Mnemiopsis leidyi in

the North Sea. Helgoland Marine Research 61: 153–155.

Brodeur, R. D., M. B. Decker, L. Ciannelli, J. E. Purcell, N.

A. Bond, P. J. Stabeno, E. Acuna& G. L. Hunt, 2008. Rise and

fall of jellyfish in the eastern Bering Sea in relation to climate

regime shifts. Progress in Oceanography 77: 103–111.

Brotz, L., 2011. Changing jellyfish populations: trends in Large

Marine Ecosystems. Fisheries Centre Research Report 19(5),

Fisheries Centre, University of British Columbia, Vancouver.

Buchanan, B. G. & E. H. Shortliffe (eds), 1984. Rule-Based

Expert Systems - The MYCIN Experiments of the Stan-

ford Heurisitic Programming Project. Addison-Wesley,

Menlo Park.

Cheung, W. W. L., R. Watson, T. Morato, T. J. Pitcher & D.

Pauly, 2007. Intrinsic vulnerability in the global fish catch.

Marine Ecology Progress Series 333: 1–12.

Chudnow, R., 2008. Are jellyfish populations increasing

worldwide (and why)? Honours Bachelor’s Thesis, Dal-

housie University, Halifax.

Coleman, R., 2010. Jellyfish, fluorescent proteins, Nobel Prizes

and pioneers in histochemistry. Acta Histochemica 112:

113–117.

Cox, E., 1999. The Fuzzy Systems Handbook: A Practitioner’s

Guide to Building, Using, and Maintaining Fuzzy Systems.

AP Professional, San Diego.

Dawson, M., 2010. Cryptic ecology. Presentation, Third Inter-

national Jellyfish Blooms Symposium, Mar del Plata, July

16, 2010.

Dawson, M. N. & W. M. Hamner, 2009. A character-based

analysis of the evolution of jellyfish blooms: adaptation

and exaptation. Hydrobiologia 616: 193–215.

Dawson, M. N., A. Sen Gupta & M. H. England, 2005. Coupled

biophysical global ocean model and molecular genetic

analyses identify multiple introductions of cryptogenic

species. Proceedings of the National Academy of Sciences

of the United States of America 102: 11968–11973.

Dovel, W. L., 1964. An approach to sampling estuarine mac-

roplankton. Chesapeake Science 5: 77–90.

Faasse, M. A. & K. M. Bayha, 2006. The ctenophore Mnemi-opsis leidyi in coastal waters of the Netherlands: an

unrecognized invasion? Aquatic Invasions 1: 270–277.

Fossa, J. H., 1992. Mass occurrence of Periphylla periphylla in a

Norwegian fjord. Sarsia 77: 237–251.

Gershwin, L. A., M. De Nardi, K. D. Winkel & P. J. Fenner,

2010. Marine stingers: review of an under-recognized

global coastal management issue. Coastal Management 38:

22–41.

Graham, W. M. & K. M. Bayha, 2007. Biological invasions by

marine jellyfish. In Nentwig, W. (ed.), Biological Invasions.

Ecological Studies, Vol. 193. Springer, Berlin: 239–255.

Greve, W., 1994. The 1989 German invasion of Muggiaeaatlantica. ICES Journal of Marine Science 51: 355–358.

Greve, W., F. Reiners & J. Nast, 1996. Biocoenotic changes of

the zooplankton in the German Bight: the possible effects

of eutrophication and climate. ICES Journal of Marine

Science 53: 951–956.

Greve, W., F. Reiners, J. Nast & S. Hoffmann, 2004. Helgoland

Roads meso- and macrozooplankton time-series 1974 to

2004: lessons from 30 years of single spot, high frequency

sampling at the only off-shore island of the North Sea.

Helgoland Marine Research 58: 274–288.

Haddad, M. A. & M. Nogueira, 2006. Reappearance and sea-

sonality of Phyllorhiza punctata medusae in southern

Brazil. Revista Brasileira De Zoologia 23: 824–831.

Haddock, S. H. D., 2004. A golden age of gelata: past and future

research on planktonic ctenophores and cnidarians. Hyd-

robiologia 530: 549–556.

Haddock, S. H. D., 2008. Reconsidering evidence for potential

climate-related increases in jellyfish. Limnology and

Oceanography 53: 2759–2762.

Hagadorn, J. W., R. H. Dott & D. Damrow, 2002. Stranded on a

late Cambrian shoreline: medusae from central Wisconsin.

Geology 30: 147–150.

Hamner, W. M. & M. N. Dawson, 2009. A review and synthesis

on the systematics and evolution of jellyfish blooms:

advantageous aggregations and adaptive assemblages.

Hydrobiologia 616: 161–191.

Hansson, H. G., 2006. Ctenophores of the Baltic and adjacent

Seas – the invader Mnemiopsis is here! Aquatic Invasions

1: 295–298.

Hay, S., 2006. Marine ecology: gelatinous bells may ring

change in marine ecosystems. Current Biology 16:

R679–R682.

Hay, S. J., J. R. G. Hislop & A. M. Shanks, 1990. North Sea

scyphomedusae – summer distribution, estimated biomass

and significance particularly for 0-group Gadoid fish.

Netherlands Journal of Sea Research 25: 113–130.

Heinle, D. R., 1965. A screen for excluding jellyfish and cte-

nophores from Clarke-Bumpus plankton samples. Chesa-

peake Science 6: 231–232.

Hoffmann, E., 2005. Fisk, fiskeri og epifauna – Limfjorden

1984–2004. Report 147-05, Danish Institute for FisheriesResearch, Charlottenlund: 19 pp (in Danish).

Holland, B. S., M. N. Dawson, G. L. Crow & D. K. Hofmann,

2004. Global phylogeography of Cassiopea: molecular

evidence for cryptic species and multiple invasions of the

Hawaiian Islands. Marine Biology 145: 1119–1128.

Hosia, A., 2007. Gelatinous zooplankton in western Norwegian

fjords: ecology, systematics and comparisons with adjacent

waters. PhD Thesis, University of Bergen, Bergen.

Hsieh, Y. H. P., F. M. Leong & J. Rudloe, 2001. Jellyfish as

food. Hydrobiologia 451: 11–17.

Ishii, H., T. Ohba & T. Kobayasi, 2008. Effects of low dissolved

oxygen on planula settlement, polyp growth and asexual

reproduction of Aurelia aurita. Plankton & Benthos

Research 3: 107–113.

Jørgensen, S. E., 2008. Overview of the model types available

for development of ecological models. Ecological Model-

ling 215: 3–9.

Kendall, D., 1990. Shrimp retention characteristics of the

Morrison soft TED – a selective webbing exclusion panel

inserted in a shrimp trawl net. Fisheries Research 9: 13–21.

Lee, C. C., 1990. Fuzzy logic in control systems: fuzzy logic

controller – Part I. IEEE Transactions on Systems, Man,

and Cybernetics 20: 404–418.


123

Lee, C. I., E. Pakhomov, A. Atkinson & V. Siegel, 2010. Long-

term relationships between the marine environment, krill

and salps in the Southern Ocean. Journal of Marine Biology

2010, Article ID 410129: 18 pp.

Licandro, P., D. V. P. Conway, M. N. D. Yahia, M. L. F. de

Puelles, S. Gasparini, J. H. Hecq, P. Tranter & R. R. Kirby,

2010. A blooming jellyfish in the northeast Atlantic and

Mediterranean. Biology Letters 6: 688–691.

Lo, W. T., J. E. Purcell, J. J. Hung, H. M. Su & P. K. Hsu, 2008.

Enhancement of jellyfish (Aurelia aurita) populations by

extensive aquaculture rafts in a coastal lagoon in Taiwan.

ICES Journal of Marine Science 65: 453–461.

Loeb, V., V. Siegel, O. Holm-Hansen, R. Hewitt, W. Fraser, W.

Trivelpiece & S. Trivelpiece, 1997. Effects of sea-ice

extent and krill or salp dominance on the Antarctic food

web. Nature 387: 897–900.

Lynam, C. P., S. J. Hay & A. S. Brierley, 2004. Interannual

variability in abundance of North Sea jellyfish and links to

the North Atlantic Oscillation. Limnology and Oceanog-

raphy 49: 637–643.

Lynam, C. P., S. J. Hay & A. S. Brierley, 2005. Jellyfish

abundance and climatic variation: contrasting responses in

oceanographically distinct regions of the North Sea, and

possible implications for fisheries. Journal of the Marine

Biological Association of the United Kingdom 85:

435–450.

Mackie, G. O., 1974. Locomotion, floatation, and dispersal. In

Muscatine, L. & H. M. Lenhoff (eds), Coelenterate Biol-

ogy: Reviews and New Perspectives. Academic Press, New

York: 313–357.

Macrokanis, C. J., N. L. Hall & J. K. Mein, 2004. Irukandji

syndrome in northern Western Australia: an emerging

health problem. Medical Journal of Australia 181:

699–702.

Maranger, R., N. Caraco, J. Duhamel & M. Amyot, 2008.

Nitrogen transfer from sea to land via commercial fisheries.

Nature Geoscience 1: 111–113.

Matsushita, Y. & N. Honda, 2006. Method of designing and

manufacturing JET (Jellyfish Excluder for Towed fishing

gear) for various towed fishing gears. Bulletin of Fisheries

Research Agency 16: 19–27 (in Japanese with English

abstract).

Mianzan, H. W. & R. A. Guerrero, 2000. Environmental pat-

terns and biomass distribution of gelatinous macrozoo-

plankton. Three study cases in the South-western Atlantic

Ocean. Scientia Marina 64: 215–224.

Mills, C. E., 1995. Medusae, siphonophores, and ctenophores as

planktivorous predators in changing global ecosystems.

ICES Journal of Marine Science 52: 575–581.

Mills, C. E., 2001. Jellyfish blooms: are populations increasing

globally in response to changing ocean conditions? Hyd-


Møller, L. F. & H. U. Riisgard, 2007a. Impact of jellyfish and

mussels on algal blooms caused by seasonal oxygen

depletion and nutrient release from the sediment in a

Danish fjord. Journal of Experimental Marine Biology and

Ecology 351: 92–105.

Møller, L. F. & H. U. Riisgard, 2007b. Population dynamics,

growth and predation impact of the common jellyfish

Aurelia aurita and two hydromedusae, Sarsia tubulosa,

and Aequorea vitrina in Limfjorden (Denmark). Marine

Ecology Progress Series 346: 153–165.

Nagata, R. M., M. A. Haddad & M. Nogueira, 2009. The nui-

sance of medusae to shrimp trawls in central part of

southern Brazilian Bight, from the perspective of artisanal

fishermen. Pan-American Journal of Aquatic Sciences 4:

312–325.

Nomura, H. & T. Ishimaru, 1998. Monitoring the occurrence of

medusae and ctenophores in Tokyo Bay, central Japan, in

recent 15 years. Oceanography in Japan 7: 99–104 (in

Japanese with English abstract).

Ohta, N., M. Sato, K. Ushida, M. Kokubo, T. Baba, K. Tanig-

uchi, M. Urai, K. Kihira & J. Mochida, 2009. Jellyfish

mucin may have potential disease-modifying effects on

osteoarthritis. BMC Biotechnology 9: 98.

Oliveira, O. M. P., 2007. The presence of the ctenophore

Mnemiopsis leidyi in the Oslofjorden and considerations on

the initial invasion pathways to the North and Baltic Seas.

Aquatic Invasions 2: 185–189.

Omori, M. & W. M. Hamner, 1982. Patchy distribution of

zooplankton: behavior, population assessment and sam-

pling problems. Marine Biology 72: 193–200.

Palomares, M. L. D. & D. Pauly, 2009. The growth of jellyf-

ishes. Hydrobiologia 616: 11–21.

Pauly, D., 1995. Anecdotes and the shifting base-line syndrome

of fisheries. Trends in Ecology & Evolution 10: 430.

Pauly, D., W. M. Graham, S. Libralato, L. Morissette & M. L. D.

Palomares, 2009a. Jellyfish in ecosystems, online dat-

abases, and ecosystem models. Hydrobiologia 616: 67–85.

Pauly, D., J. Alder, S. Booth, W. W. L. Cheung, V. Christensen,

C. Close, U. R. Sumaila, W. Swartz, A. Tavakolie, R.

Watson, L. Wood & D. Zeller, 2009b. Fisheries in Large

Marine Ecosystems: descriptions and diagnoses. In Sher-

man, K. & G. Hempel (eds), The UNEP Large Marine

Ecosystem Report: A Perspective on Changing Conditions

in LMEs of the World’s Regional Seas. United Nations

Environment Programme Regional Seas Report and Stud-

ies No. 182, Nairobi: 23–40.

Pierce, J., 2009. Prediction, location, collection and transport of

jellyfish (Cnidaria) and their polyps. Zoo Biology 28:

163–176.

Pramod, G., 2010. Illegal, unreported and unregulated marine

fish catches in the Indian Exclusive Economic Zone. Field

Report, Policy and Ecosystem Restoration in Fisheries,

Fisheries Centre, University of British Columbia, Van-

couver: 30 pp.

Pugh, P. R., 1989. Gelatinous zooplankton – the forgotten fauna.

Progress in Underwater Science 14: 67–78.

Purcell, J. E., 2005. Climate effects on formation of jellyfish and

ctenophore blooms: a review. Journal of the Marine Bio-

logical Association of the United Kingdom 85: 461–476.

Purcell, J. E., 2009. Extension of methods for jellyfish and

ctenophore trophic ecology to large-scale research. Hyd-


Purcell, J. E., 2012. Jellyfish and ctenophore blooms coincide

with human proliferations and environmental perturba-

tions. Annual Review of Marine Science 4: 209–235.

Purcell, J. E. & M. N. Arai, 2001. Interactions of pelagic cni-

darians and ctenophores with fish: a review. Hydrobiologia

451: 27–44.


123

Purcell, J. E., S. Uye & W. T. Lo, 2007. Anthropogenic causes of

jellyfish blooms and their direct consequences for humans:

a review. Marine Ecology Progress Series 350: 153–174.

Quinones, J., H. Mianzan, M. Acha, S. Purca & W. M. Graham,

2010. El Viejo, El Nino and jellyfishes: dramatic envi-

ronmental-driven changes in jellyfish abundances off Peru.

Presentation, Third International Jellyfish Blooms Sym-

posium, Mar del Plata, July 15, 2010.

Richardson, A. J., A. Bakun, G. C. Hays & M. J. Gibbons, 2009.

The jellyfish joyride: causes, consequences and manage-

ment responses to a more gelatinous future. Trends in

Ecology & Evolution 24: 312–322.

Riisgard, H. U., L. Bøttiger, C. V. Madsen & J. Purcell, 2007.

Invasive ctenophore Mnemiopsis leidyi in Limfjorden

(Denmark) in late summer 2007 – assessment of abundance

and predation effects. Aquatic Invasions 2: 395–401.

Riisgard, H. U., P. Andersen & E. Hoffmann, 2012. From fish to

jellyfish in the eutrophicated Limfjorden (Denmark).

Estuaries and Coasts. doi:10.1007/s12237-012-9480-4.

Schluter, M. H., A. Merico, M. Reginatto, M. Boersma, K.

H. Wiltshire & W. Greve, 2010. Phenological shifts of

three interacting zooplankton groups in relation to climate

change. Global Change Biology 16: 3144–3153.

Sherman, K. & G. Hempel (eds), 2009. The UNEP Large Marine

Ecosystem Report: A Perspective on Changing Conditions

in LMEs of the World’s Regional Seas. United Nations

Environment Programme Regional Seas Report and Stud-

ies No. 182, Nairobi: 872 pp.

Sherman, K. & Q. Tang (eds), 1999. Large Marine Ecosystems

of the Pacific Rim: Assessment, Sustainability, and Man-

agement. Blackwell Science, Cambridge: 465 pp.

Sørnes, T. A., D. L. Aksnes, U. Bamstedt & M. J. Youngbluth,

2007. Causes for mass occurrences of the jellyfish Pe-riphylla periphylla: a hypothesis that involves optically

conditioned retention. Journal of Plankton Research 29:

157–167.

Spinner, K., 2010. Currents steering sea nettles our way. The

Herald Tribune, Sarasota County, October 20, 2010.

Sugahara, T., M. Ueno, Y. Goto, R. Shiraishi, M. Doi, K. Ak-

iyama & S. Yamauchi, 2006. Immunostimulation effect of

jellyfish collagen. Bioscience, Biotechnology, and Bio-

chemistry 70: 2131–2137.

Tendal, O. S., K. R. Jensen & H. U. Riisgard, 2007. Invasive

ctenophore Mnemiopsis leidyi widely distributed in Danish

waters. Aquatic Invasions 2: 455–460.

UNESCO, 1968. Zooplankton sampling. United Nations Edu-

cational, Scientific and Cultural Organization, Paris: 174

pp.

Uye, S. & U. Ueta, 2004. Recent increase of jellyfish popula-

tions and their nuisance to fisheries in the Inland Sea of

Japan. Bulletin of the Japanese Society of Fisheries

Oceanography 68: 9–19. (in Japanese with English

abstract).

Uye, S., M. Shimizu & T. Watanabe, 2010. Tackling the giant

jellyfish (Nemopilema nomurai) plague: cause, forecast

and countermeasure. Plenary presentation, Third Interna-

tional Jellyfish Blooms Symposium, Mar del Plata, July 14,

2010.

van der Werf, H. M. G. & C. Zimmer, 1998. An indicator of

pesticide environmental impact based on a fuzzy expert

system. Chemosphere 36: 2225–2249.

van Walraven, L., V. T. Langenberg & H. W. van der Veer,

2010. Major changes in occurrence, seasonal patterns and

species composition of gelatinous zooplankton in the

western Dutch Wadden Sea from 1960 until present. Pre-

sentation, Third International Jellyfish Blooms Sympo-

sium, Mar del Plata, July 14, 2010.

Ward, J. M., J. E. Kirkley, R. Metzner & S. Pascoe, 2004.

Measuring and assessing capacity in fisheries. 1. Basic

concepts and management options. FAO Fisheries Tech-

nical Paper No. 433/1. Food and Agriculture Organization,

Rome: 40 pp.

Watson, R., A. Kitchingman, A. Gelchu & D. Pauly, 2004.

Mapping global fisheries: sharpening our focus. Fish and

Fisheries 5: 168–177.

Young, G. A. & J. W. Hagadorn, 2010. The fossil record of

cnidarian medusae. Palaeoworld 19: 212–221.

Youngbluth, M. J. & U. Bamstedt, 2001. Distribution, abun-

dance, behavior and metabolism of Periphylla periphylla, a

mesopelagic coronate medusa in a Norwegian fjord.

Hydrobiologia 451: 321–333.

Zadeh, L. A., 1965. Fuzzy sets. Information and Control 8:

338–353.

Zimmer, M., 2005. Glowing Genes: A Revolution in Biotech-

nology. Prometheus Books, Amherst.


123

http://dx.doi.org/10.1007/s12237-012-9480-4

Increasing jellyﬁsh populations: trends in Large Marine ......Anglia, Norwich NR4 7TJ, UK W. W. L....

Documents

Transcript of Increasing jellyﬁsh populations: trends in Large Marine ......Anglia, Norwich NR4 7TJ, UK W. W. L....